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Generating JSON Directly from Postgres
PostgreSQL has built-in JSON generators that can be used to create structured JSON output right in the database, upping performance and radically simplifying web tiers.
Topics /
Databases & Data
The most popular blog posts about databases & data on Hacker News. 314 posts.
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GCP Outpaces Azure, AWS in the 2021 Cloud Report
Now in its third year, the 2021 Cloud Report from Cockroach Labs compares AWS, Azure, and Google (GCP) to tell realistic performance stories on behalf of mission-critical OLTP applications.
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How we built a serverless SQL database
CockroachDB serverless is the first truly scalable serverless SQL database. Learn how it works and how a serverless sql database can relieve the operational burden of developers.
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How to Become a Data Engineer in 2021
The demand for data engineers is growing rapidly. According to hired.com, the demand has increased by 45% in 2019. The median salary for Data Engineers in SF Bay Area is around $160k. So the question…
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Memcached vs. Redis – More Different Than You Would Expect
Insights from the Kablamo Engineering Team
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MinIO: A Bare Metal Drop-In for AWS S3
Benchmarks & Tips for Big Data, Hadoop, AWS, Google Cloud, PostgreSQL, Spark, Python & More...
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Last Mile Redis
Documentation and guides from the team at Fly.io.
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Why SELECT * is bad for SQL performance (2020)
Here’s a list of reasons why SELECT * is bad for SQL performance, assuming that your application doesn’t actually need all the columns. When I write production code, I explicitly specify the columns of interest in the select-list (projection), not only for performance reasons, but also for application reliability…
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Why I Use Nim instead of Python for Data Processing
Lazy programmers often prefer to substitute computing effort for programming effort.
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FoundationDB: A distributed unbundled transactional key value store
FoundationDB: A Distributed Unbundled Transactional Key Value Store Zhou, et al., 2021
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RabbitMQ Streams Overview
RabbitMQ 3.9 introduces a new type of data structure: streams. Streams unlock a set of use cases that could have been tedious to implement with "traditional" queues. Let's discover in this post how streams expand the capabilities of RabbitMQ.
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Investigating how the New York Times A/B tests their headlines
Part 1 of a series on the New York Times, in which I take a close look at how (and when) the New York Times tests multiple headlines for a single article.
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Hierarchical Structures in PostgreSQL (2020)
Modelling hierarchical/team/categorical/tag data with arbitrary depths.
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PlanetScale is now generally available
PlanetScale is now GA.
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Comparing SQLite, DuckDB and Arrow with UN trade data
This is not a competition, is just to show how to use the hardware with relative efficiency, being the idea is to show something collaborative rather than competitive.
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Why do we use R rather than Excel?
I recently had cause to take a beginners course in R - a language I'm fairly familiar with. One of the other students had never used it before, so we were buddied up in order for me to show them the ropes. The first lesson of R is always the same. Read a CSV, manipulate it a bit, draw a graph. We did it all without…
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Rally, a novel privacy-first data sharing platform
Mozilla teams up with Princeton University researchers to enable crowdsourced science for public good; collaborates with research groups at Princeton, Stan
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Spending $5k to learn how database indexes work
At Superwall we’re building an SDK to help App Developers increase their revenue by showing the right offer to the right person at the right time. Follow me on twitter for little tips and articles like this one.
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Partitioning GitHub’s relational databases to handle scale
In 2019, to meet growth and availability challenges, we set a plan in motion to improve our tooling and ability to partition relational databases.
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Which color scale to use when visualizing data
This is part 1 of a series on “Which color scale to use when visualizing data” (Part …
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Why isn't differential dataflow more popular?
Differential dataflow is a library that lets you write simple dataflow programs and a) then runs them in parallel and b) efficiently updates the outputs when new inputs arrive. Compared to competition like spark and kafka streams , it can handle more complex computations and provides dramatically better throughput and…